Category: computer vision

3D reconstruction is one of those tasks that depends on getting many things right. If you usually have a bad reconstruction or a very weird looking one, chances are that you might have an issue with one of the issues in this list. I strongly advice using openCV when doing 3D reconstruction, as it provides a robust set of functions for each step of 3D reconstruction. These issues in the list are still valid even if you don’t use openCV for reconstruction. Without further ado:

1. Calibration Errors

The calibration is the MOST important step. This can not be said enough. It will serve you well double or triple check it. If everything goes right, you will usually end with an error less 0.5. Anything larger than 1.0 is usually a sign of things going bad. Make sure to use findCheckerboardCorners, then optimize it further with cornerSubPix. Any slight calibration problem will blow up and make your reconstruction fall flat on its face.

2. Calibration Pad Orientation

This is one problem that I faced and it has no solution except to take another image. If you use a square checkerboard pattern you may get this issue. I was informed that using a rectangle pad avoids this issue (as it checks for a set number of corners to detect per side). Whenever you calibrate double check that by visualizing the detected checkerboard corners that your orientation happens in the same order. Usually it’s from top left to bottom right. As seen in the image above, sometimes, one checkerboard patterns starts from top right to bottom left. This is a weird situation and I found no solution to this problem, except taking new images.

3. Ordering of calibration points

Look in to the 2D points and the 3D points and make sure they correspond in position the way you want them to correspond. I prefer them increasing and decreasing in the same order, i.e. when x of 2D points increase, x of 3D points also increase, and when y of 2D points increase then y of 3D points increase. If you also prefer it this way you usually have to flip the Y coordinate of the 2D points. There is no hard and fast rule about this, but make sure it 3D-2D pairs correspond the way you think they correspond.

4. Reprojection Error

After you get your camera matrices make sure to check for the re-projection error. Like the calibration error this is a good indicator of something going wrong. This is usually less than 2.0 to get a decent reconstruction.

5. Automatic Feature Detection

As cool as the Flann detection scheme is, there’s a reason that active 3D reconstruction methods are still in business. These automatic detectors rarely give you the points you want with the accuracy you want. If you closely check them, you will notice that there are errors of a pixel range, so tread carefully.

5. Triangulation Error

After you triangulate points with your correspondence point, again check the re-projection error. They have to correspond to your correspondence points. This not happening again is clear indication that something is amiss. Mapping the texture from these re-projected points is also a great way to find out whether something is wrong. The texture usually gets warped and small errors can easily be detected.

6. Image resolution

Another problem that I faced when reconstructing objects was with small image resolution. When using an image of 640 x 480 for performance reasons, I noticed that my 3D reconstruction had noticeable errors. After double-checking everything I noticed that my resolution wasn’t accurate enough to wipe out perturbations of 3D points. Switching to a high resolution such as 1600 x 1200, solved this problem. This problem can be detected because other than small but noticeable errors in the reconstruction, the shape and the textures will be preserved well. This is a good indication (given that the correspondence is done well) that the image resolution used is not high enough.

I didn’t use a rig. I just used a hand held camera as this was a temporary exercise. But, if you are going to calibrate once and use the rig throughout, you need to make sure to re-calibrate again. As pointed out by Kabir, the tiniest bit of warping or bending of the rig will screw up reconstruction.

8. Synchronizing frames between cameras

If you are using multiple images (or video), then you need to make sure that the cameras are synchronized. Otherwise, you may end up using different image pairs! As Oliver pointed out if you’re moving the cameras around and the frames aren’t captured at the same time it gives the effect of them not being rigidly linked. Usually USB cameras require an external clock/trigger signal to sync start of image integration. Firewire cameras usually sync across the bus themselves.

I hope these pointers helped some of you to iron out any issues you had with 3D reconstruction using a stereo rig. Please let me know in the comments section of any other issues you faced when doing 3D reconstruction.